{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Purchased</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15624510</td>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>19000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15810944</td>\n",
       "      <td>Male</td>\n",
       "      <td>35</td>\n",
       "      <td>20000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15668575</td>\n",
       "      <td>Female</td>\n",
       "      <td>26</td>\n",
       "      <td>43000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15603246</td>\n",
       "      <td>Female</td>\n",
       "      <td>27</td>\n",
       "      <td>57000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15804002</td>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>76000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>15691863</td>\n",
       "      <td>Female</td>\n",
       "      <td>46</td>\n",
       "      <td>41000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>15706071</td>\n",
       "      <td>Male</td>\n",
       "      <td>51</td>\n",
       "      <td>23000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>15654296</td>\n",
       "      <td>Female</td>\n",
       "      <td>50</td>\n",
       "      <td>20000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>15755018</td>\n",
       "      <td>Male</td>\n",
       "      <td>36</td>\n",
       "      <td>33000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>15594041</td>\n",
       "      <td>Female</td>\n",
       "      <td>49</td>\n",
       "      <td>36000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>400 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      User ID  Gender  Age  EstimatedSalary  Purchased\n",
       "0    15624510    Male   19            19000          0\n",
       "1    15810944    Male   35            20000          0\n",
       "2    15668575  Female   26            43000          0\n",
       "3    15603246  Female   27            57000          0\n",
       "4    15804002    Male   19            76000          0\n",
       "..        ...     ...  ...              ...        ...\n",
       "395  15691863  Female   46            41000          1\n",
       "396  15706071    Male   51            23000          1\n",
       "397  15654296  Female   50            20000          1\n",
       "398  15755018    Male   36            33000          0\n",
       "399  15594041  Female   49            36000          1\n",
       "\n",
       "[400 rows x 5 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = pd.read_csv(r'C:\\Users\\碌卡\\Desktop\\python编程学习\\python数据分析\\100daysML_file\\100day\\datasets\\Social_Network_Ads.csv')\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = dataset.iloc[:,[2,3]].values\n",
    "Y = dataset.iloc[:,4].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,Y_train,Y_test = train_test_split(X,Y,test_size = 0.25,random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "F:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\validation.py:475: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.fit_transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "classifier = LogisticRegression()\n",
    "classifier.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 1 0 0 0 0 0 0 1 0 0 0 0\n",
      " 0 0 1 0 0 0 0 1 0 0 1 0 1 1 0 0 1 1 0 0 0 0 0 0 1 0 1 0 1 0 0 0 1 1 0 0 0\n",
      " 0 0 1 0 1 1 1 1 0 0 1 1 0 1 0 0 0 1 0 0 0 0 0 0 1 1]\n",
      "0.88\n"
     ]
    }
   ],
   "source": [
    "y_pred = classifier.predict(X_test)\n",
    "score = classifier.score(X_test,Y_test)\n",
    "print(y_pred)\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(Y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.colors import ListedColormap\n",
    "X_set,Y_set = X_train,Y_train\n",
    "X1,X2 = np.meshgrid(np.arange(start=X_set[:,0].min()-1,stop=X_set[:,0].max()+1,step=0.01)\n",
    "                   ,np.arange(start=X_set[:,1].min()-1,stop=X_set[:,1].max()+1,step=0.01))\n",
    "plt.contourf(X1,X2\n",
    "            ,classifier.predict(np.array([X1.ravel(),X2.ravel()]).T).reshape(X1.shape)\n",
    "            ,alpha = 0.75\n",
    "            ,cmap = ListedColormap(('red','green')))\n",
    "plt.xlim(X1.min(),X1.max())\n",
    "plt.ylim(X2.min(),X2.max())\n",
    "for i,j in enumerate(np.unique(Y_set)):\n",
    "    plt.scatter(X_set[Y_set==j,0]\n",
    "               ,X_set[Y_set==j,1]\n",
    "               ,c = ListedColormap(('red','green'))(i)\n",
    "               ,label=j)\n",
    "plt.title('LOGISTIC(Training set)')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Estimated Salary')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_set,Y_set = X_test,Y_test\n",
    "X1,X2 = np.meshgrid(np.arange(start=X_set[:,0].min()-1,stop=X_set[:,0].max()+1,step=0.01)\n",
    "                   ,np.arange(start=X_set[:,1].min()-1,stop=X_set[:,1].max()+1,step=0.01))\n",
    "plt.contourf(X1,X2\n",
    "            ,classifier.predict(np.array([X1.ravel(),X2.ravel()]).T).reshape(X1.shape)\n",
    "            ,alpha = 0.75\n",
    "            ,cmap = ListedColormap(('red','green')))\n",
    "plt.xlim(X1.min(),X1.max())\n",
    "plt.ylim(X2.min(),X2.max())\n",
    "for i,j in enumerate(np.unique(Y_set)):\n",
    "    plt.scatter(X_set[Y_set==j,0]\n",
    "               ,X_set[Y_set==j,1]\n",
    "               ,c = ListedColormap(('red','green'))(i)\n",
    "               ,label=j)\n",
    "plt.title('LOGISTIC(Training set)')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Estimated Salary')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "34dfee18d5f4a96df9a8fcc719c91cf50e8ed50de2aa108bf45cd20982063274"
  },
  "kernelspec": {
   "display_name": "Python 3.7.0 64-bit ('base': conda)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
